Top Use Cases of Big Data Analytics in Different Businesses Industries
Big data analytics provides a competitive edge for business organizations across various industries. It enables businesses to transform and improves operations by minimizing risks, reduce overheads and overall improve performance.
As mentioned in previous blog posts, thanks to the advancement of new technologies such as cloud computing and IoT (Internet of Things), business organizations can now capture and store vast amounts of data to produce actionable insights through Big Data Analytics - in order to make informed business decisions.
According to the 2018 Big Data Analytics Market Study by Dresner Advisory Services, Big data adoption in enterprises rose from 17% in 2015 to 59% in 2018, a very impressive increase reaching a Compound Annual Growth Rate (CAGR) of 36%.
Additionally, in 2019, IDC forecasts that worldwide revenues for big data and business analytics will reach $189.1 billion and is expected which is an increase of 12% over 2018, a firm indication showing that Big Data is continuing its strong growth. IDC also predicts that revenue for Big Data and Business Analytics worldwide will reach over $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7%.
In the recent week, the news of Salesforce acquiring Tableau (Data Visualization) for $15.3B, and Google acquiring Looker for $2.6B also show how prevalent Analytics has become as well as its importance for businesses in the digital age.
In this article, we examine the top five big data analytics use cases in different industries transforming businesses.
Big Data Analytics Use Cases in Businesses
Big Data Analytics can be applied across various business industries such as Financial Services, E-commerce and Retail, Healthcare, Market Research, and others. Big Data Analytics allows businesses to make use of real-time data and make better-informed business decisions.
Below are some of the business areas that Big Data Analytics can help transform and improve upon:
- Sales and Marketing: customer segmentation, customer relationship management, and offering more personalized customer care program and marketing campaigns; launching of new products and services;
- Purchasing: Inventory management and inventory levels forecast;
- Administration: optimizing internal resources and reducing costs;
- Financial services: fraud detection; mitigating risks;
- Machinery operation: identifying and removing performance bottlenecks proactively;
- Others: predicting equipment failures;
- Maintenance and support: Identifying the causes of failures and problems in real-time.
Top 5 Big Data analytics use cases:
- Fraud detection
One popular use case of big data analytics is Banks and financial institutions leverage this technology to monitor millions of credit card transactions, to protect credit cards holder from fraudulent transactions on a daily basis.
For an example of credit card transactions, based on historical data patterns, a sophisticated fraud prevention system of predictive analytics or machine learning might be able to assess if the business transactions performed by the credit card holder is typical of such holder and determine if the transaction is likely to be fraudulent or not.
- Credit assessment
Along with fraud detection, banks together with financial institutions and insurance companies can also leverage the massive amount of data by applying data science models to assess the creditworthiness of borrowers. For example, financial institutions can obtain data regarding the credit history of borrowers from various sources such as telecom or utility bills and apply analytics to determine creditworthiness. As a result, if borrowers have a bad credit history leading to a low rating score, banks wouldn’t grant the loan. This helps financial institutions to reduce credit risks.
In e-commerce, by analyzing customers’ past purchase history data and shopping behavior, companies can discover various hidden patterns and offered a more personalized marketing program as well as recommending the products and services items that customers are more likely to purchase, thus boosting sales and effectiveness.
With big data analytics, inventory management can move beyond the traditional methods of analyzing historical data on sales and stocks out. By applying data science and analytics with algorithms exploring the relationship between different data elements, retailers can produce insights into customers behavior, product demand level, in-store and online sales performance, and more.
Specifically, by leveraging big data analytics, retailers can forecast the level of product demand, thus plan and optimize their inventory for maximum efficiency. This helps retails stores to allocate resources efficiently, avoiding waste of resources and money on the inventory that’s not required as well as reducing warehouse space cost.
- Transportation and logistics
Data Science and Big Data analytics can help transportation providers and logistics companies to optimize routing and freight movement by recommending the optimal route and time for delivery, reducing the time taken, saving on petrol, and subsequently increasing productivity.
Moreover, data science and Big Data analytics can also help businesses proactively enhance vehicle performance and maintenance through a real-time view of fleet operating conditions and metrics. This helps eliminate bottlenecks and improve efficiency.
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